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Genetic variants of calcium and vitamin D metabolism in kidney stone disease
Sarah A. Howles D.Phil., F.R.C.S.(Urol), Akira Wiberg B.M.B.Ch., Michelle
Goldsworthy Ph.D., Asha L. Bayliss Ph.D., Emily Grout R.G.N., Chizu Tanikawa
Ph.D., Yoichiro Kamatani M.D., Ph.D., Chikashi Terao M.D., Ph.D., Atsushi
Takahashi Ph.D., Michiaki Kubo M.D., Ph.D., Koichi Matsuda M.D., Ph.D., Rajesh
V. Thakker M.D., F.R.C.P., F.R.S., Benjamin W. Turney D.Phil., F.R.C.S.(Urol), and
Dominic Furniss D.M., F.R.C.S.(Plast).
Nuffield Department of Surgical Sciences, University of Oxford, United Kingdom
(S.A.H., M.G., E.G., B.W.T.), Nuffield Department of Orthopaedics, Rheumatology
and Musculoskeletal Sciences, University of Oxford, United Kingdom (A.W., D.F.),
Academic Endocrine Unit, Radcliffe Department of Medicine, University of Oxford,
United Kingdom (S.A.H., M.G., A.B., R.V.T.), Laboratory of Genome Technology,
Human Genome Centre, University of Tokyo, Japan (C.Ta.), RIKEN Centre for
Integrative Medical Sciences, Kanagawa, Japan (Y.K., C.Te., A.T., M.K.), Laboratory
of Clinical Genome Sequencing, Department of Computational Biology and Medical
Sciences, University of Tokyo, Japan (K.M.).
Drs Sarah A. Howles and Akira Wiberg contributed equally to this article.
Dr Benjamin W. Turney and Prof. Dominic Furniss contributed equally to this article.
Address correspondence to Dr. Howles at the Oxford Centre for Diabetes,
Endocrinology and Metabolism (OCDEM), Churchill Hospital, Oxford, OX3 7LE,
United Kingdom, or at sarah.howles@nds.ox.ac.uk
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Kidney stone disease (nephrolithiasis) is a major clinical and economic health
burden1,2 with a heritability of ~45-60%3. To identify genetic variants associated
with nephrolithiasis we performed genome-wide association studies (GWAS) and
meta-analysis in British and Japanese populations, including 12,123
nephrolithiasis cases and 416,928 controls. Twenty loci associated with
nephrolithiasis were identified, ten of which are novel. A novel CYP24A1 locus is
predicted to affect vitamin D metabolism and five loci, DGKD, DGKH, WDR72,
GPIC1, and BCR, are predicted to influence calcium-sensing receptor (CaSR)
signaling. In a validation cohort of nephrolithiasis patients the CYP24A1-
associated locus correlated with serum calcium concentration and number of
kidney stone episodes, and the DGKD-associated locus correlated with urinary
calcium excretion. Moreover, DGKD knockdown impaired CaSR-signal
transduction in vitro, an effect that was rectifiable with the calcimimetic
cinacalcet. Our findings indicate that genotyping may inform risk of incident
kidney stone disease prior to vitamin D supplementation and facilitate precision-
medicine approaches, by targeting CaSR-signaling or vitamin D activation
pathways in patients with recurrent kidney stones.
Kidney stones affect ~20% of men and ~10% of women by 70 years of age1 and
commonly cause debilitating pain. The prevalence of this disorder is increasing and
the United States is predicted to spend over $5 billion per year by 2030 on its
treatment2. Unfortunately, up to 50% of individuals will experience a second kidney
stone episode within 10 years of their initial presentation4 and recurrent stone disease
is linked to renal function decline5.
Twin studies have reported a heritability of >45% and >50% for stone disease and
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hypercalciuria, respectively3,6 and a strong family history of urolithiasis, including a
parent and a sibling, results in a standard incidence ratio (SIR) for stone formation of
>50 in contrast to a SIR of 1.29 in spouses7. Three genome-wide association studies
of nephrolithiasis have been published, identifying six loci associated with disease8-10.
However, only two loci (CLDN14 and RGS14-SLC34A1) have been replicated and no
trans-ethnic studies have been undertaken. To increase understanding of the common
genetic factors contributing to risk of nephrolithiasis, genome-wide association
studies and meta-analysis were undertaken using UK Biobank and Biobank Japan
resources11,12, integrating data from 12,123 stone formers and 416,928 controls.
Twenty genetic loci associating with nephrolithiasis were identified, 10 of which were
initially identified from the UK Biobank discovery cohort (Supplementary Table 5)
and another 10 from a subsequent trans-ethnic meta-analysis with Japanese GWAS
summary statistics (Table 1, Fig. 1, and Supplementary Fig. 1)13. Fifty-four candidate
genes were identified via in silico analysis of these 20 loci based on FUMA positional
mapping, functional annotation, and biological plausibility14. MAGMA gene-property
analysis implemented in FUMA revealed a striking overexpression of these genes in
the kidney cortex; the GENE2FUNC tool demonstrated enrichment for gene
ontologies associated with transmembrane ion transport, renal function, and calcium
homeostasis, including “response to vitamin D” (Supplementary Fig. 2-3 and Table
7)15.
Population Attributable Risk (PAR) was calculated at each locus using data from the
UK Biobank population, and ranged from 1.3% to 22.3% (Supplementary Table 6),
indicating the importance of these variants in the pathogenesis of nephrolithiasis at a
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population level. The highest PAR was identified with a CYP24A1-associated locus
and five of the identified loci, with PAR scores between 5.9-7.9%, were predicted to
influence CaSR-signaling. These loci were selected for further analysis.
rs17216707 is ~38kb upstream of CYP24A1, a gene encoding cytochrome P450
family 24 subfamily A member 1 (CYP24A1), an enzyme that metabolises active
1,25-dihydroxyvitamin D to inactive 24,25-dihydroxyvitamin D. Loss-of-function
mutations in CYP24A1 cause autosomal recessive infantile hypercalcaemia type 1
(OMIM 126065)16. We postulated that the CYP24A1 increased-risk allele associates
with decreased CYP24A1 activity, leading to perturbations of calcium homeostasis
and mimicking an attenuated form of infantile hypercalcaemia type 1. Therefore,
associations of rs17216707 with serum calcium, phosphate, parathyroid hormone
(PTH), and 25-hydroxyvitamin D concentrations, and urinary calcium excretion, and
number of kidney stone episodes were sought in a validation cohort of kidney stone
formers. 1,25-hydroxyvitamin D levels were unavailable. Reference ranges for 24-
hour urinary calcium excretion differ for men and women17, thus associations with
urinary calcium excretion were therefore examined separately.
Individuals homozygous for the CYP24A1 increased-risk allele rs17216707 (T) had a
significantly increased mean serum calcium concentration when compared to
heterozygotes, consistent with a recessive effect (mean serum calcium 2.36mmol/l
(TT) vs. 2.32mmol/l (TC)) (Table 2). rs17216707 (T) homozygotes had more kidney
stone episodes than heterozygotes (mean number of stone episodes 4.0 (TT) vs. 2.4
(TC), p=0.0003) and there was a significant correlation across genotypes (TT vs. TC
vs. CC) with number of stone episodes (p=0.0024)(Table 2). No correlation was
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found between rs17216707 genotype and serum phosphate, PTH, 25-hydroxyvitamin
D concentration or urinary calcium excretion.
These findings support our hypothesis that the rs17216707 increased-risk allele is
associated with a relative hypercalcaemia and reduced activity of the 24-hydroxylase
enzyme and highlight the role of vitamin D catabolism in kidney stone formation.
Patients with loss-of-function CYP24A1 mutations have been successfully treated
with inhibitors of vitamin D synthesis including fluconazole, similar therapies may be
useful in rs17216707 (TT) recurrent kidney stone formers18. Furthermore, vitamin D
supplementation in patients with biallelic loss-of-function mutations in CYP24A1
cause nephrocalcinosis16. We predict that rs17216707 (TT) individuals may similarly
display an increased sensitivity to vitamin D. The National Institute of Clinical
Excellence (NICE) recommends that all adults living in the UK should take daily
vitamin D supplementation. However, our findings suggest that supplementation may
put individuals homozygous for the CYP24A1 increased-risk allele at risk of kidney
stones.
A previously reported association between the CaSR-associated intronic SNP
rs7627468 and nephrolithiasis was confirmed (p=3.5x10-5)9. In addition, five of the
identified loci are linked to genes that are predicted to influence CaSR signaling.
rs13003198 is ~6kb upstream of DGKD, encoding diacylglycerol kinase delta
(DGKD); rs1037271 is an intronic variant in DGKH, encoding diacylglycerol kinase
eta (DGKH). DGKD and DGKH phosphorylate diacylglcerol, a component of the
intracellular CaSR-signaling pathway inducing CaSR-mediated membrane ruffling
and activating protein kinase C signaling cascades including MAPK19,20(Fig. 2).
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rs578595 is an intronic variant in WDR72 encoding WD repeat domain 72 (WDR72)
and rs3760702 is ~300bp upstream of GIPC1 that encodes Regulator of G-protein
signaling 19 Interacting Protein 1 (GIPC1). Both WDR72 and GIPC1 are thought to
play a role in clathrin-mediated endocytosis, a process central to sustained
intracellular CaSR signaling20-23(Fig. 2). rs13054904 is ~110kb upstream of BCR,
encoding a RAC1 (Rac Family Small GTPase 1) GTPase-activating protein known as
Breakpoint Cluster Region (BCR)24. RAC1 activation is induced by CaSR ligand
binding and mediates CaSR-induced membrane ruffling19(Fig. 2).
The CaSR plays a central role in calcium homeostasis, increasing renal calcium
reabsorption and stimulating PTH release to enhance bone resorption, urinary calcium
reabsorption, and renal synthesis of 1,25-dihydroxyvitamin D25. Gain-of-function
mutations in components of the CaSR-signaling pathway result in autosomal
dominant hypocalcaemia (ADH, OMIM 601198, 615361), which is associated with
hypercalciuria in ~10% of individuals26,27. ADH-associated mutations result in a
gain-of-function in CaSR-intracellular signaling in vitro via pathways including intra-
cellular calcium ions or MAPK26,28,29. We hypothesized that the nephrolithiasis-
associated loci linked to CaSR-signaling associate with enhanced CaSR signal
transduction resulting in a biochemical phenotype mimicking an attenuated form of
ADH. DGKD and DGKH were selected for further analysis based on their PAR
scores and the potential to influence both membrane ruffling and MAPK CaSR-
signaling pathways (Fig.2).
The DGKD increased-risk allele rs838717 (G) (top DGKD-associated SNP in the UK
Biobank GWAS, Supplementary Table 4, linkage disequilibrium with rs13003198
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r2=0.53) associated with increased 24-hour urinary calcium excretion in male stone
formers (mean 24-hour urinary calcium excretion 7.27mmol (GG) vs. 4.54mmol
(AA), p=0.0055) (Table 2) consistent with enhanced CaSR-signal transduction. No
association of the DGKD increased-risk allele rs838717 (G) with urinary calcium
excretion was identified in female stone formers, which is likely due to a lack of
power as a result of the small sample size, however it is interesting to note that
heritability of stone disease is lower in women than in men3. Furthermore, no
correlations were observed between genotype and serum calcium, phosphate, PTH,
25-hydroxyvitamin D concentrations or number of stone episodes despite
hypocalcemia being the prominent phenotype in ADH patients. This may relate to
differential tissue expression patterns of DGKD or the specificity of DGKD effects on
intracellular CaSR-signaling pathways.
The DGKH increased-risk allele rs1170174 (A) (top DGKH-associated SNP in the
UK Biobank GWAS, Supplementary Table 4, linkage disequilibrium with
rs13003198 r2=0.3) did not associate with biochemical phenotype or stone recurrence.
However the sample size of homozygotes was small (AA, n=6) and prior to
Bonnferonni correction, a suggestive association was detected was detected with
urinary calcium excretion in male stone formers (mean 24-hour urinary calcium
excretion 8.14mmol (AA) vs. 5.09mmol (GG), p=0.0503).
To investigate the role of DGKD in CaSR-signalling, HEK-CaSR-SRE cells were
treated with scrambled or DGKD targeted siRNA and intracellular MAPK responses
to alterations in extracellular calcium concentration assessed. SRE responses were
significantly decreased in cells with reduced DGKD expression (DGKD-KD) when
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compared to cells with baseline DGKD expression (WT) (maximal response DGKD-
KD=5.28 fold change, 95% confidence interval (CI)=4.77-5.79 vs. WT=7.20 fold
change, 95% CI=6.46-7.93, p=0.0065). Cinacalcet, rectified this loss-of-function
(DGKD-KD+5nM cinacalcet, maximal response=7.62 fold change, 95% CI=5.98-
9.27)(Fig. 2A-E). These findings provide evidence that DGKD influences CaSR-
mediated signal transduction and suggest that the DGKD increased-risk allele may
associate with a relative increase in DGKD expression thereby enhancing CaSR-
mediated signal transduction. Calcilytics, including NPS-2143 and ronacaleret, rectify
enhanced CaSR-mediated signaling in vitro and biochemical phenotypes in mouse
models of ADH29-31 and therefore may represent novel, targeted therapies for
recurrent stone formers carrying CaSR-associated increased-risk alleles.
In conclusion, this study has identified 20 loci linked to kidney stone formation, 10 of
which are novel, and revealed the importance of vitamin D metabolism pathways and
enhanced CaSR-signaling in the pathogenesis of nephrolithiasis. Our findings suggest
a role for genetic testing to identify individuals in whom vitamin D supplementation
should be used with caution and to facilitate a precision-medicine approach for the
treatment of recurrent kidney stone disease, whereby targeting of the CaSR-signaling
or vitamin D metabolism pathways may be beneficial in the treatment of a subset of
patients with nephrolithiasis.
References
1. Scales, C. D., Smith, A. C., Hanley, J. M., Saigal, C. S.Urologic Diseases in America Project. Prevalence of Kidney Stones in the United States. Eur. Urol. 62, 160–165 (2012).
2. Antonelli, J. A., Maalouf, N. M., Pearle, M. S. & Lotan, Y. Use of the National Health and Nutrition Examination Survey to calculate the impact of obesity and diabetes on cost and prevalence of urolithiasis in
.CC-BY 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted January 30, 2019. . https://doi.org/10.1101/515882doi: bioRxiv preprint
10
2030. Eur. Urol. 66, 724–729 (2014). 3. Goldfarb, D. S., Avery, A. R., Beara-Lasic, L., Duncan, G. E. &
Goldberg, J. A Twin Study of Genetic Influences on Nephrolithiasis in Women and Men. Kidney Int Rep (2018). doi:10.1016/j.ekir.2018.11.017
4. Pearle, M. S. et al. Medical management of kidney stones: AUA guideline. The Journal of Urology 192, 316–324 (2014).
5. Gambaro, G. et al. The Risk of Chronic Kidney Disease Associated with Urolithiasis and its Urological Treatments: A Review. The Journal of Urology 198, 268–273 (2017).
6. Hunter, D. J. et al. Genetic contribution to renal function and electrolyte balance: a twin study. Clin. Sci. 103, 259–265 (2002).
7. Hemminki, K. et al. Familial risks in urolithiasis in the population of Sweden. BJU Int. 121, 479–485 (2018).
8. Thorleifsson, G. et al. Sequence variants in the CLDN14 gene associate with kidney stones and bone mineral density. Nat. Genet. 41, 926–930 (2009).
9. Oddsson, A. et al. Common and rare variants associated with kidney stones and biochemical traits. Nat Commun 6, 7975 (2015).
10. Urabe, Y. et al. A genome-wide association study of nephrolithiasis in the Japanese population identifies novel susceptible Loci at 5q35.3, 7p14.3, and 13q14.1. PLoS Genet. 8, e1002541 (2012).
11. Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).
12. Hirata, M. et al. Overview of BioBank Japan follow-up data in 32 diseases. J Epidemiol 27, S22–S28 (2017).
13. Tanikawa, C. et al. GWAS identifies nine nephrolithiasis susceptibility loci related with metabolic and crystallization pathways. BioRvix doi.org/10.1101/519553
14. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8, 1826 (2017).
15. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
16. Schlingmann, K. P. et al. Mutations in CYP24A1 and idiopathic infantile hypercalcemia. N Engl J Med 365, 410–421 (2011).
17. Curhan, G. C., Willett, W. C., Speizer, F. E. & Stampfer, M. J. Twenty-four-hour urine chemistries and the risk of kidney stones among women and men. Kidney Int. 59, 2290–2298 (2001).
18. Sayers, J. et al. Successful treatment of hypercalcaemia associated with a CYP24A1 mutation with fluconazole. Clin Kidney J 8, 453–455 (2015).
19. Schlam, D. & Canton, J. Every day I‘m rufflin’: Calcium sensing and actin dynamics in the growth factor-independent membrane ruffling of professional phagocytes. Small GTPases 8, 65–70 (2017).
20. Gorvin, C. M. et al. AP2σ Mutations Impair Calcium-Sensing Receptor Trafficking and Signaling, and Show an Endosomal Pathway to Spatially Direct G-Protein Selectivity. Cell Rep 22, 1054–1066 (2018).
21. Wang, S.-K. et al. Critical roles for WDR72 in calcium transport and matrix protein removal during enamel maturation. Mol Genet Genomic Med 3, 302–319 (2015).
.CC-BY 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted January 30, 2019. . https://doi.org/10.1101/515882doi: bioRxiv preprint
11
22. Shang, G. et al. Structure analyses reveal a regulated oligomerization mechanism of the PlexinD1/GIPC/myosin VI complex. Elife 6, 213 (2017).
23. Nesbit, M. A. et al. Mutations in AP2S1 cause familial hypocalciuric hypercalcemia type 3. Nat. Genet. (2012). doi:10.1038/ng.2492
24. Smith, K. R., Rajgor, D. & Hanley, J. G. Differential regulation of the Rac1 GTPase-activating protein (GAP) BCR during oxygen/glucose deprivation in hippocampal and cortical neurons. J Biol. Chem. 292, 20173–20183 (2017).
25. Hannan, F. M., Babinsky, V. N. & Thakker, R. V. Disorders of the calcium-sensing receptor and partner proteins: insights into the molecular basis of calcium homeostasis. J. Mol. Endocrinol. 57, R127–42 (2016).
26. Nesbit, M. A. et al. Mutations affecting G-protein subunit α11 in hypercalcemia and hypocalcemia. N Engl J Med 368, 2476–2486 (2013).
27. Piret, S. E. et al. Identification of a G-Protein Subunit-α11 Gain-of-Function Mutation, Val340Met, in a Family With Autosomal Dominant Hypocalcemia Type 2 (ADH2). J. Bone Miner. Res. 31, 1207–1214 (2016).
28. Pearce, S. H. et al. A familial syndrome of hypocalcemia with hypercalciuria due to mutations in the calcium-sensing receptor. N Engl J Med 335, 1115–1122 (1996).
29. Gorvin, C. M. et al. Gα11 mutation in mice causes hypocalcemia rectifiable by calcilytic therapy. JCI Insight 2, e91103 (2017).
30. Hannan, F. M. et al. The Calcilytic Agent NPS 2143 Rectifies Hypocalcemia in a Mouse Model With an Activating Calcium-Sensing Receptor (CaSR) Mutation: Relevance to Autosomal Dominant Hypocalcemia Type 1 (ADH1). Endocrinology 156, 3114–3121 (2015).
31. Babinsky, V. N. et al. Mutant Mice With Calcium-Sensing Receptor Activation Have Hyperglycemia That Is Rectified by Calcilytic Therapy. Endocrinology 158, 2486–2502 (2017).
Acknowledgements This work was supported by grants from Kidney Research UK
(RP_030_20180306) to S.A.H., A.W., M.G., B.W.T., and D.F, National Institute for
Health Research (N.I.H.R) Oxford Biomedical Research Centre to R.V.T, and the
Wellcome Trust (204826/z/16/z) to S.A.H, and M.G. S.A.H. is a N.I.H.R Academic
Clinical Lecturer. A.W is a MRC Clinical Research Training Fellow. R.V.T. has
Senior Investigator Awards from the Wellcome Trust (106995/z/15/z) and N.I.H.R.
(NF-SI-0514-10091). We acknowledge the contribution to this study made by the
Oxford Centre for Histopathology Research and the Oxford Radcliffe Biobank, which
are supported by the NIHR Oxford Biomedical Research Centre.
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Author Contributions
S.A.H., A.W., B.W.T., and D.F. designed this study. S.A.H, A.W., M.G., A.L.B.,
E.G., C.Ta., Y.K., C.Te., A.T., M.K., K.M., and B.W.T acquired data. A.W. and D.F.
carried out genetic association analysis. S.A.H, M.G., and A.L.B undertook in vitro
studies. S.A.H., A.W., M.G, C.Ta., Y.K., C.Te., A.T., M.K., K.M., R.V.T, B.W.T.,
and D.F analysed and interpreted data. S.A.H., A.W., R.V.T., B.W.T., and D.F wrote
the first draft of the manuscript. All other co-authors participated in the preparation of
the manuscript by reading and commenting on the draft prior to submission.
Completing Financial Interests
The authors declare no competing financial interests.
.CC-BY 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted January 30, 2019. . https://doi.org/10.1101/515882doi: bioRxiv preprint
Figure 1. Results of trans-ethnic genome-wide association study in kidney stone disease. A trans-ethnic meta-analysis of kid
disease was performed for 12,123 patients with kidney stone disease and 416,928 controls from the UK Biobank and BioBank Japan.
a quantile-quantile plot of observed vs. expected p-values. The λGC demonstrated some inflation (1.0957), but the LD score regressio
intercept of 0.9997, with an attenuation ratio of 0.0075 indicated that the inflation was largely due to polygenicity and the large sa
Panel B is a Manhattan plot showing the genome-wide p values (-log10) plotted against their respective positions on each of the autos
horizontal red line shows the genome-wide significance threshold of 5.0x10-8.
13
kidney stone
n. Panel A is
sion (LDSC)
sample size.
tosomes. The
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certified by peer review) is the author/funder. It is m
ade available under a The copyright holder for this preprint (w
hich was not
this version posted January 30, 2019. .
https://doi.org/10.1101/515882doi:
bioRxiv preprint
Figure 2. Schematic model for CaSR signaling
Ligand binding of calcium ions (yellow) by the G protein coupled receptor CaSR
(gray) results in G protein-dependent stimulation via Gαq/11 (green) or Gαi/o (blue)
causing stimulation of intracellular signaling pathways including intracellular calcium
([Ca2+]i) release, MAPK stimulation or cAMP reduction. Gαq/11 signals via inositol
1,4,5-trisphosphate (IP3) and diacylglycerol (DAG). DAG leads to protein kinase C
(PKC) stimulation along with RAC activation, which results in membrane ruffling.
Following calcium ion binding the CaSR is internalized via clathrin-mediated
endocytosis where signaling continues via the endosome. Proteins postulated to
influence CaSR-signaling and their potential sites of action are shown in red.
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Figure 3. CaSR-mediated SRE responses following DGKD knockdown and effect
of cinacalcet treatment in HEK-CaSR-SRE cells. Panel A shows relative
expression of DGKD, as assessed by quantitative real-time PCR of HEK-CaSR-SRE
cells treated with scrambled (WT) or DGKD (DGKD-KD) siRNA and used for SRE
experiments. Samples were normalized to a geometric mean of four housekeeper
genes: PGK1, GAPDH, TUB1A, CDNK1B. n=8. Panel B shows a representative
western blot of lysates from HEK-CaSR cells treated with scrambled or DGKD
siRNA and used for SRE experiments. PGK1 was used as a loading control. Panel C
shows the relative expression of DGKD, as assessed by densitometry of western blots
from cells treated with scrambled or DGKD siRNA demonstrating a ~50% reduction
in expression of DGKD following treatment with DGKD siRNA. Samples were
normalized to PGK1. All cells expressed CaSR. n=6. Panel D shows SRE responses
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of HEK-CaSR-SRE cells in response to changes in extracellular calcium
concentration. Cells were treated with scrambled (WT) or DGKD (DGKD-KD)
siRNA. The responses ± SEM are shown for 8 independent transfections for WT and
DGKD-KD cells and 4 independent transfections for DGKD-KD + 5nM cinacalcet
cells. Treatment with DGKD siRNA led to a reduction in maximal response (red line)
compared to cells treated with scrambled siRNA (black line). This loss-of-function
could be rectified by treatment with 5nM cinacalcet (blue line). Post desensitization
points were not included in the analysis (grey, light red, and light blue). Panel E
demonstrates the mean maximal responses with SEM of cells treated with scrambled
siRNA (WT, black), DGKD siRNA (DGKD-KD, red) and DGKD siRNA incubated
with 5nM cinacalcet (blue). Statistical comparisons of maximal response were
undertaken using F test. Students T tests were used to compare relative expression.
Two-way ANOVA was used to compare points on dose response curve with reference
to WT. Data are shown as mean ±SEM with **p<0.01, ****p<0.0001.
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Table 1. SNPs significantly associated with kidney stone disease at trans-ethnic meta-analysis
SNP Discovery GWAS in UK Biobank Replication GWAS in BioBank Japan Meta-Analysis Chromosome SNP Positiona EAb NEAc EAFd INFOe ORf P EAFd INFOe ORf P ORf P Candidate
genes 1 rs10917002 21836340 T C 0.11 0.997 1.18 (1.12-1.25) 3.60×10-9 0.38 0.998 1.09 (1.04-1.15) 5.83×10-5 1.13 (1.09-1.17) 3.45×10-11 ALPL
2 rs780093 27742603 T C 0.38 1 1.08 (1.04-1.12) 3.60×10-5 0.56 0.997 1.14 (1.09-1.18) 1.10×10-8 1.10 (1.08-1.13) 1.31×10-13 GCKR
2 rs13003198 234257105 T C 0.39 0.997 1.10 (1.06-1.14) 6.50×10-8 0.25 0.98 1.12 (1.06-1.18) 1.09×10-5 1.11 (1.07-1.14) 3.89×10-11 DGKD
4 rs1481012 89039082 G A 0.11 0.994 1.12 (1.06-1.18) 4.30×10-5 0.30 0.994 1.11 (1.05-1.17) 1.50×10-5 1.11 (1.07-1.16) 2.79×10-8 ABCG2
5 rs56235845 176798040 G T 0.33 0.986 1.16 (1.12-1.20) 9.10×10-15 0.31 0.87 1.18 (1.12-1.25) 1.88×10-11 1.16 (1.13-1.20) 2.64×10-21 SLC34A1
6 rs1155347 39146230 C T 0.22 0.975 1.12 (1.07-1.17) 2.60×10-7 0.16 0.925 1.16 (1.08-1.24) 1.33×10-6 1.13 (1.09-1.17) 8.54×10-11 KCNK5
6 rs77648599 160624115 G T 0.03 0.992 1.33 (1.21-1.47) 5.50×10-9 0.04 0.739 1.22 (1.06-1.44) 1.89×10-3 1.30 (1.20-1.42) 5.39×10-10 SLC22A2
7 rs12539707 27626165 T C 0.30 0.999 1.13 (1.08-1.17) 6.30×10-10 0.09 0.789 1.10 (1.01-1.21) 0.0268 1.12 (1.08-1.16) 1.09×10-10 HIBADH
7 rs12666466 30916430 G C 0.03 0.994 1.22 (1.11-1.34) 5.00×10-5 0.12 0.989 1.17 (1.08-1.26) 2.80×10-6 1.19 (1.12-1.26) 3.26×10-8 AQP1
11 rs4529910 111243102 T G 0.27 0.998 1.07 (1.02-1.11) 1.40×10-3 0.59 0.999 1.12 (1.08-1.16) 3.94×10-7 1.09 (1.06-1.12) 4.25×10-10 POU2AF1
13 rs1037271 42779410 C T 0.39 0.995 1.11 (1.07-1.15) 2.50×10-8 0.55 0.936 1.20 (1.15-1.24) 7.49×10-15 1.15 (1.12-1.18) 1.29×10-24 DGKH
15 rs578595 53997089 C A 0.46 0.996 1.09 (1.05-1.13) 2.50×10-6 0.69 0.996 1.11 (1.06-1.15) 2.25×10-5 1.09 (1.07-1.12) 6.26×10-11 WDR72
16 rs77924615 20392332 A G 0.20 0.980 1.13 (1.08-1.18) 1.80×10-8 0.22 0.984 1.17 (1.10-1.24) 2.80×10-9 1.14 (1.10-1.19) 1.14×10-13 UMOD
16 rs889299 23381914 G A 0.76 1 1.10 (1.05-1.14) 8.20×10-6 0.66 0.895 1.09 (1.04-1.14) 9.39×10-4 1.09 (1.06-1.13) 1.55×10-8 SCNN1B
17 rs1010269 59448945 G A 0.83 0.981 1.08 (1.03-1.14) 7.10×10-4 0.56 0.87 1.17 (1.12-1.22) 4.82×10-11 1.13 (1.10-1.17) 3.71×10-15 BCAS3
17 rs4793434 70352537 G C 0.50 0.993 1.09 (1.05-1.13) 1.50×10-6 0.32 0.983 1.09 (1.04-1.15) 2.04×10-4 1.09 (1.06-1.12) 4.52×10-9 SOX9
19 rs3760702 14588237 A G 0.33 0.994 1.08 (1.05-1.13) 1.40×10-5 0.25 0.971 1.14 (1.08-1.20) 3.78×10-7 1.09 (1.07-1.13) 1.98×10-9 GIPC1
20 rs17216707 52732362 T C 0.81 0.961 1.17 (1.12-1.22) 9.90×10-12 0.92 0.766 1.24 (1.15-1.34) 5.90×10-6 1.19 (1.14-1.23) 7.82×10-18 CYP24A1
21 rs12626330 37835982 G C 0.49 0.980 1.16 (1.12-1.20) 5.80×10-17 0.39 0.981 1.12 (1.07-1.18) 2.77×10-7 1.15 (1.12-1.18) 7.24×10-21 CLDN14
22 rs13054904 23410918 A T 0.26 0.999 1.15 (1.11-1.20) 3.30×10-12 0.02 0.967 1.05 (0.91-1.26) 0.505 1.14 (1.10-1.19) 4.49×10-12 BCR
aBased on NCBI Genome Build 37 (hg19). bThe effect allele. cThe alternate (non-effect) allele. dThe effect allele frequency in the study population. eThe imputation quality score. fOdds ratio (95% confidence intervals).
OR>1 indicative of increased risk with effect allele.
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Table 2: Genotype-phenotype correlations in cohort of kidney stone formers
Variable Normal range§ CYP24A1 (rs17216707) DGKD (rs838717) TT TC CC AA AG GG
Serum Calcium (mmol/l)
2.10-2.50 2.36±0.01* (260)
2.32±0.01 (109)
2.34±0.02 (15)
2.34±0.01 (107)
2.35±0.01 (182)
2.36±0.01 (95)
Phosphate (mmol) 0.7-1.40 1.02±0.13 (274)
1.02±0.02 (111)
0.99±0.05 (14)
1.03±0.02 (114)
1.01±0.02 (193)
1.02±0.02 (92)
Parathyroid hormone (pmol/l)
1.3-7.6 5.06±0.18 (271)
5.59±0.32 (107)
5.27±0.64 (14)
5.34±0.32 (108)
5.38±0.23 (189)
4.72±0.23 (95)
25-hydroxy vitamin D (nmol/l)
>50 54.8±1.84 (227)
50.6±2.60 (89)
55.7±6.92 (10)
56.1±3.13 (88)
52.6±2.04 (157)
53.2±2.90 (81)
Urine Male patients 24hr calcium excretion (mmol)
<7.5 6.11±0.47 (77)
4.82±0.55 (33)
3.101.27 (3)
4.54±0.45* (33)
5.45±0.48 (57)
7.27±0.91 (25)
Female patients 24hr calcium excretion (mmol)
<6.2 4.85±0.53 (31)
5.06±0.56 (15)
4.27±1.69 (2)
5.83±1.34 (9)
4.92±0.46 (27)
4.12±0.56 (12)
Number stone episodes - 4.0±0.4*
(287) 2.4±0.2
(19) 2.4±0.4
(14) 3.5±0.41
(114) 3.8±0.43
(208) 2.8±0.26
(98) Numbers of stone forming patients included in analysis are shown in parentheses. Serum calcium values are albumin-adjusted. All values are expressed as mean ±SEM. Students
T-tests were used for comparisons between groups of parametric data, Mann-Whitney-U tests were used for comparison of non-parametric data (number of stone episodes).
Anova tests was used for comparisons of multiple sets of parametric data, no significance was reached. Kruskall-Wallis tests were used for comparisons of multiple sets of non-
parametric data (number of stone episodes), significance at p=0.0024 was reached for CYP24A1 locus correlations. *Denotes significance on comparison to bold cohort within
group at Bonferroni corrected threshold of p<0.05/7 = 0.007. §Normal ranges are from Nesbit et al26 and Curhan et al17.
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Online Methods
Study participants
UK Biobank: For the UK-based GWAS, the UK Biobank resource was utilised, a
prospective cohort study of ~500,000 individuals from the UK, aged between 40-69, who
have had whole-genome genotyping undertaken, and have allowed linkage of these data
with their medical records1,2. ICD-10 and OPCS codes were used to identify individuals
with a history of nephrolithiasis (Supplementary Table 1). Patients were excluded if they
were recorded to have a disorder of calcium homeostasis, malabsorption, or other condition
known to predispose to kidney stone disease (Supplementary Table 2). Following quality
control (QC) 6,536 UK Biobank participants were identified as cases and 388,508
individuals as controls (Supplementary Table 3).
Japanese patients: Diagnosis of nephrolithiasis was confirmed by enrolling physicians;
patients with bladder stones were excluded. DNA samples of 5,587 nephrolithiasis
patients were obtained from BioBank Japan3. Controls (28,870 individuals) were identified
from four population-based cohorts, including the JPHC (Japan Public Health Center)-
based prospective study4, the J-MICC (Japan Multi-Institutional Collaborative Cohort)
study5, IMM (Iwate Tohoku Medical Megabank Organization) and ToMMo (Tohoku
Medical Megabank Organization)6.
Validation cohort: Patients attending the Oxford University Hospitals NHS Foundation
Trust for treatment of kidney stones were enrolled into the Oxford University Hospitals
NHS Foundation Trust Biobank of Kidney Stone Formers, following informed consent.
Clinical data including urological history, past medical history and family history was
recorded along with details of serum and urinary biochemistry. Whole blood and urine
samples were stored at -80°C.
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Ethical approval
UK Biobank has approval from the North West Multi-Centre Research Ethics Committee
(11/NW/0382), and this study (“Epidemiology of Kidney Stone Disease”) has UK Biobank
study ID 885. Ethical committees at each Japanese institute approved the project.
Collection of clinical data and biological samples from kidney stone patients attending the
Oxford University Hospitals NHS Foundation Trust was approved under the Oxford
Radcliffe Biobank research tissue bank ethics (09/H0606/5+5). All patients provided
written informed consent.
Genotyping
UK Biobank: The genotyping, QC and imputation methodology employed by UK Biobank
are described in detail elsewhere7. Briefly, UK Biobank contains genotypes of 488,377
participants who were genotyped on two very similar genotyping arrays: UK BiLEVE
Axiom Array (807,411 markers; 49,950 participants), and UK Biobank Axiom Array
(825,927 markers; 438,427 participants). The two arrays are very similar, sharing
approximately 95% of marker content. Genotypes were called from the array intensity data,
in 106 batches of approximately 4700 samples each using a custom genotype-calling
pipeline.
Japanese samples: Samples were genotyped with Illumina HumanOmniExpressExome
BeadChip or a combination of the Illumina HumanOmniExpress and HumanExome
BeadChips8,9.
Validation cohort: DNA was extracted from whole blood samples using the Maxwell 16
Tissue DNA purification kit (Promega). SNP genotyping was undertaken using TaqMan
SNP Genotyping Assays (ThermoFisher) and Type-it® Fast SNP Probe PCR Kit (Qiagen).
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Quality Control
UK Biobank: Quality Control (QC) was performed using PLINK10 v1.9 and R v3.3.1. All
SNPs with a call rate <90% were removed, accounting for the two different genotyping
platforms used to genotype the individuals. Sample-level QC was undertaken and
individuals excluded with one or more of the following: (1) call rate <98%, (2) discrepancy
between genetically inferred sex (Data Field 22001) and self-reported sex (Data Field 31),
or individuals with sex chromosome aneuploidy (Data Field 22019), (3) heterozygosity >3
S.D. from the mean (calculated using UK Biobank’s PCA-adjusted heterozygosity values,
Data Field 20004). Individuals were then excluded who were not flagged by UK Biobank
as having white British ancestry (on the basis of principal component analysis and self-
reporting as “British” – Data Field 22006). Data was merged with publicly available data
from the 1000 Genomes Project5,11 and principal components analysis (PCA) performed
using flashpca12 to confirm that the white British ancestry individuals from UK Biobank
overlapped with the “GBR” individuals from the 1000 Genomes Project. BOLT-LMM was
used in analysis and therefore there were no sample exclusions based on relatedness13. in
total, 86,693 individuals were excluded based on the above criteria. SNP-level QC was
performed by excluding SNPs with Hardy-Weinberg equilibrium (HWE) p<10-4, <98%
call rate, and minor allele frequency (MAF)<1%. Overall, 237,245 SNPs were excluded in
total. Finally, six individuals were excluded who harboured an abnormal number of SNPs
with a minor allele count of 1, or were visual outliers when autosomal heterozygosity was
plotted against call rate. This resulted in a final dataset of 401,667 individuals and 547,011
SNPs. Following QC, individuals were excluded who were recorded to have a disorder of
calcium homeostasis, malabsorption, or other condition known to predispose to kidney
stone disease (Supplementary Table 2). Subsequent case ascertainment was performed
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using the list of ICD-10 and OPCS codes for kidney and ureteric stones (Supplementary
Table 1).
Japanese Samples: Samples were excluded if (i) call rate� was <�0.98, (ii) they were
from closely related individuals identified by identity-by-descent analysis, (iii) the samples
were sex-mismatched with a lack of information, or (iv) they were non–East Asian outliers
identified by principal component analysis of the studied samples and the three major
reference populations (Africans, Europeans, and East Asians) in the International HapMap
Project14. Standard quality-control criteria for variants were applied, excluding those with
(i) SNP call rate�<�0.99, (ii) minor allele frequency�<�1%, and (iii) Hardy–Weinberg
equilibrium P value < 1.0�×�10−6.
Imputation
UK Biobank data: UK Biobank’s method of phasing and imputation of SNPs is described
in detail elsewhere7. Briefly, phasing on the autosomes was performed using SHAPEIT315,
using the 1000 Genomes Phase 3 dataset as a reference panel. For imputation, both the
HRC (Haplotype Reference Consortium) reference panel16 and a merged UK10K / 1000
Genomes Phase 3 panel were used. This resulted in a dataset with 92,693,895 autosomal
SNPs, short indels and large structural variants. Imputation files were released in the
BGEN (v1.2) file format.
Japanese data: Genotypes were prephased with MACH17 and dosages imputed with
minimac and the 1000 Genomes Project Phase 1 (version 3) East Asian reference
haplotypes11.
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Association Analysis
UK Biobank data: Genome-wide association analysis was undertaken across 547,011
genotyped SNPs and ~8.4 million imputed SNPs with MAF≥0.01 and Info Score≥0.9,
using a linear mixed non-infinitesimal model implemented in BOLT-LMM v2.318. A
reference genetic map file for hg19 and a reference linkage disequilibrium (LD) score file
for European-ancestry individuals included in the BOLT-LMM package in the analysis was
used. Three covariates were used in the association study: genetic sex, age, and the
genotyping platform (to account for array effects). The LD score regression intercept11 of
0.9997 with an attenuation ratio of 0.0075 indicated minimal inflation when adjusted for
the large sample size. Conditional analysis at each associated locus was performed by
conditioning on the allelic dosage (calculated using QCTOOL v2) of the most significantly
associated SNP at each locus.
Japanese data: GWAS was conducted using a logistic regression model by incorporating
age, sex, and the top 10 principal components as covariates.
Trans-ethnic Meta-analysis: Trans-ethnic meta-analysis was performed using the summary
statistics from the UK and Japanese GWAS data sets (12,123 cases and 416,928 controls).
An imputation quality score (RSQR) threshold of >0.5 was applied to the Japanese GWAS
SNPs19 prior to performing a fixed-effects meta-analysis using GWAMA20, using ~5
million SNPs common to both GWAS datasets. Quantile-quantile and Manhattan plots
were created using FUMA21.
In Silico Analyses
The summary statistics from the GWAS meta-analysis were analysed in FUMA21 v1.3.3c,
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selecting UK Biobank Release 2 (White British) as the population reference panel (as the
vast majority of individuals in the meta-analysis are of British rather than Japanese
ethnicity). Functionally annotated SNPs were mapped to genes based on genomic position
and annotations obtained from ANNOVAR, using positional mapping in FUMA
(Supplementary Table 7)22. MAGMA (implemented in FUMA) was used to perform a
gene-property analysis in order to identify particular tissue types relevant to kidney stones.
This analysis determines if tissue-specific differential expression levels are predictive of
the association of a gene with kidney stones, across 53 different tissues taken from the
GTEx v7 database23 (Supplementary Figure 2).
In order to gain insight into the biological pathways implicated by the FUMA-prioritised
genes, a gene set analysis was implemented using the GENE2FUNC tool in FUMA using
the 54 positionally mapped genes with unique Entrez IDs and gene symbols. The following
parameters were applied: Benjamini-Hochberg false discovery rate (FDR) for multiple
testing correction, adjusted p-value cut-off = 0.0025, minimum number of overlapped
genes = 2, GTEx v7 RNA-Seq expression data. Hypergeometric tests were performed to
test if genes of interest are overrepresented in any of the pre-defined gene sets in GO
biological processes (MsigDB v6.1) (Supplementary Figure 3).
Population Attributable Risk
To assess the impact of an identified allele of interest from the meta-analysis with risk of
kidney stone disease within the UK Biobank population, Population Attributable Risk
(PAR) was calculated for SNPs of interest using the following equations24 with individual-
level genotype data from the UK Biobank:
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Where:
SR = Stone formers with at least one increased-risk allele
SNR = Stone formers with no risk allele
CR = Control patients with at least one increased-risk allele
CNR = Control patients with no risk allele
Dosages of risk alleles for all individuals were computed using QCTOOL v2. As the
majority of SNPs were imputed rather than genotyped, allelic dosages were not always
discrete (i.e. 0, 1 or 2). In such circumstances, an allelic dosage threshold of ≥ 0.9 was
applied in order to consider an individual to possess at least one copy of the risk allele.
Genotype-Phenotype Correlations
Associations were sought between genotype and serum calcium (albumin adjusted),
phosphate, parathyroid hormone, and 25-hydroxyvitamin D, and urinary calcium excretion
and number of stone episodes. Patients were excluded from inclusion in genotype-
phenotype correlations if they were known to have a disorder of calcium homeostasis,
malabsorption, or other condition known to predispose to kidney stone disease.
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Functional characterisation of the Calcium-Sensing Receptor Pathway
Cell culture and transfection. Functional studies were undertaken using HEK293 cells that
had been transfected to stably express calcium-sensing receptors (CaSRs) and luciferase
under the control of a serum-response element (SRE) (HEK-CaSR-SRE cells). Cells were
transfected with scrambled or DGKD siRNA (Qiagen) using lipofectamine RNAiMAX
(Thermo Fisher Scientific) 72 hours before experiments. HEK-CaSR-SRE cells were
maintained in DMEM-Glutamax media (Thermo Fisher Scientific) with 10% FBS (Gibco)
and 400μg/ml geneticin (Thermo Fisher Scientific) and 200μg/ml hygromycin (Invitrogen)
at 37°C, 5% CO2.
Confirmation of DGKD knockdown: Successful knockdown of DGKD was confirmed via
quantitative reverse transcriptase PCR (qRT-PCR) and western blot analyses. qRT-PCR
analyses were performed in quadruplicate using Power SYBR Green Cells-to-CT™ Kit
(Life Technologies), DGKH, PGK1, GAPDH, TUB1A, CDNK1B specific primers (Qiagen),
and a Rotor-Gene Q real-time cycler (Qiagen Inc, Valencia, CA). Samples were normalized
to a geometric mean of four housekeeper genes: PGK1, GAPDH, TUB1A, CDNK1B.
Western blot analyses were undertaken using anti-DGKD (SAB1300472; Sigma), anti-
CaSR (5C10, ADD; ab19347; Abcam), and anti-PGK1 (ab154613; Abcam). The western
blots were visualized using an Immuno-Star Western C kit (Bio-Rad) on a Bio-Rad
Chemidoc XRS+ system and relative expression of DGKD was quantified by denistometry
using ImageJ software.
Compounds: Cinacalcet (AMG-073 HCL) was obtained from Cambridge Bioscience
(catalog CAY16042) and dissolved in DMSO prior to use in in vitro studies.
SRE Response Assays: At 60 hours post transfection, cells were incubated in 0.05% fetal
bovine serum media with 0.45mM calcium for 12 hours. At 72 hours the media was
changed to varying concentrations of extracellular calcium (0.1-5mM), with either 5nM
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27
cinacalcet or equivalent volume of DMSO, and the cells were incubated for a further 4
hours at 37°C. Cells were lysed and luciferase activity measured using Luciferase Assay
System (Promega) on the PHERAstar microplate reader (BMG Labtech). Assays were
performed in >4 biological replicates (independently transfected wells, performed on at
least 4 different days). Nonlinear regression of concentration-response curves was
performed with GraphPad Prism for determinations of maximal response and EC50 values.
Statistics
For genotype-phenotype correlations, 2-tailed Student T Tests were used for parametric
data. Mann-Whitney-U tests were used for comparison of non-parametric data (number of
stone episodes). Anova tests were used for comparisons of multiple sets of parametric data.
Kruskall-Wallis tests were used for comparisons of multiple sets of non-parametric data
(number of stone episodes). Significance was defined as p<0.05 after Bonferroni correction
for 7 tests on each set of data, thus p<0.05/7=0.007.
For in vitro studies statistical comparisons were made with 2-tailed Student T Tests and
maximal responses were compared using the F-test25.
References
1. Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
2. Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012). 3. Hirata, M. et al. Overview of BioBank Japan follow-up data in 32 diseases. J
Epidemiol 27, S22–S28 (2017). 4. Tsugane, S. & Sobue, T. Baseline Survey of JPHC Study Design and Participation
Rate. J Epidemiol 11, 24–29 (2001). 5. Hamajima, N.J-MICC Study Group. The Japan Multi-Institutional Collaborative
Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer. Asian Pac. J. Cancer Prev. 8, 317–323 (2007).
6. Kuriyama, S. et al. The Tohoku Medical Megabank Project: Design and Mission. J
.CC-BY 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted January 30, 2019. . https://doi.org/10.1101/515882doi: bioRxiv preprint
28
Epidemiol 26, 493–511 (2016). 7. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants.
bioRxiv 166298 (2017). doi:10.1101/166298 8. Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body
mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017). 9. Tanikawa, C. et al. GWAS identifies two novel colorectal cancer loci at 16q24.1 and
20q13.12. Carcinogenesis 39, 652–660 (2018). 10. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-
based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007). 11. 1000 Genomes Project Consortium et al. A global reference for human genetic
variation. Nature 526, 68–74 (2015). 12. Abraham, G. & Inouye, M. Fast principal component analysis of large-scale
genome-wide data. PLoS ONE 9, e93766 (2014). 13. Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed model
association for biobank-scale data sets. bioRxiv 194944 (2017). doi:10.1101/194944 14. International HapMap 3 Consortium et al. Integrating common and rare genetic
variation in diverse human populations. Nature 467, 52–58 (2010). 15. O'Connell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48,
817–820 (2016). 16. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation.
Nat. Genet. 48, 1279–1283 (2016). 17. Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: using sequence
and genotype data to estimate haplotypes and unobserved genotypes. Genetic Epidemiology 34, 816–834 (2010).
18. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
19. Cai, Q. et al. Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1. Nat. Genet. 46, 886–890 (2014).
20. Mägi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).
21. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8, 1826 (2017).
22. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
23. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
24. Sun, E. C. et al. Association between concurrent use of prescription opioids and benzodiazepines and overdose: retrospective analysis. BMJ 356, j760 (2017).
25. Nesbit, M. A. et al. Mutations affecting G-protein subunit α11 in hypercalcemia and hypocalcemia. N Engl J Med 368, 2476–2486 (2013).
.CC-BY 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted January 30, 2019. . https://doi.org/10.1101/515882doi: bioRxiv preprint
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